A deep learning-based social distance monitoring framework for COVID-19

Abstract

Highlights The purpose of this work is to provide a deep learning platform for social distance tracking. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. The transfer learning methodology is implemented to increase the accuracy of the model. The detection algorithm uses a pre-trained algorithm. To estimate social distance violations between people, we used an approximation of physical distance.

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

“…This technology is more relevant today because it is used to detect faces not only in static images and videos but also in real-time inspection and supervision. With the advancements of convolution neural networks ( Lawrence, Giles, Tsoi, & Back, 1997 ) and deep learning ( Ahmed, Ahmad, Rodrigues, Jeon, & Din, 2020 ), very high accuracy in image classification and object detection can be achieved.Probably because of the sudden emergence of the COVID-19 pandemic, at present, there are various facial recognition technology applied to people wearing masks. HanvonTechnology Wang et al (2020 ) reported that the accuracy of masked face recognition is about 85%.…”

Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF)

“…COVID-19 is a global challenge, requiring cross-disciplinary cooperation and research to control, in order to maintain the sustainable development of society. Computational biology, big data and artificial intelligence are being used in daily monitoring, prevention and treatment of infectious diseases ( Ahmed, Ahmad, Rodrigues, Jeon, & Din, 2020 ; Bhattacharya et al, 2020 ; Loey, Manogaran, Taha, & Khalifa, 2020 ; Zhou, Qiu, Pu, Huang, & Ge, 2020 ). Abboah-Offei et al (2021) investigated the impact face mask has had in controlling transmission of respiratory viral infections.…”

Examining the spatial and temporal relationship between social vulnerability and stay-at-home behaviors in New York City during the COVID-19 pandemic

“…The health system sustainability during the public health crisis is often associated with the quality of the whole system and its capacity to meet needs without compromising extra cost. However, inequality could further exacerbate the spread of COVID-19 and add extra burdens to the health system ( Ahmed, Ahmed, Pissarides, & Stiglitz, 2020 ; Ahmed, Ahmad, Rodrigues, Jeon, & Din, 2021 ). In the context of social vulnerability, we suggest that addressing inequality should be the first step to opening up space for new approaches to sustainability during COVID-19.…”

“…In practice, the city of Los Angeles and RMDS Lab have partnered up to host an open competition for data scientists and analysts to involving in monitoring and combating the pandemic. Several attempts have been made to propose a new approach for such endeavors, such as that Ahmed, Ahmed, Pissarides et al (2020) and Ahmed, Ahmad, Rodrigues, Jeon, & Din, 2021 developed a deep learning-based monitoring framework to detect social distancing behaviors using videos from the closed-circuit television cameras. This pandemic offers unprecedented challenges for governments to overcome but at the same time it provides a unique opportunity to reexamine our policies, to improve, and to innovate.…”